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scripts/model_info.py
Step 13 requirement: report the parameter count via an explicit command,
never just claim a number. Run with a config path OR a Models.create_model()
key (o-mini, 250m, 500m, gpt2-small, gpt2-plus, deep64, mla, ...) to see the
real breakdown for that architecture.
Usage:
PYTHONPATH=. python3 scripts/model_info.py configs/model/brain_0_2_30m.yaml
PYTHONPATH=. python3 scripts/model_info.py deep64
"""
import sys
from pathlib import Path
from brain.core.config import BrainConfig
from brain.model.brain_model import BrainForCausalLM
def main() -> None:
arg = sys.argv[1] if len(sys.argv) > 1 else "configs/model/brain_0_2_30m.yaml"
if Path(arg).exists():
config = BrainConfig.from_yaml(arg)
model = BrainForCausalLM(config)
label = arg
else:
from Models.Foundation.model_factory import create_model
model = create_model(arg)
config = model.config
label = f"create_model({arg!r})"
counts = model.count_parameters()
print(f"Brain config: {config.name} ({label})")
print("-" * 50)
for k, v in config.to_dict().items():
print(f" {k}: {v}")
print("-" * 50)
print("Parameter breakdown:")
for k, v in counts.items():
pct = f" ({100 * v / counts['total']:.1f}%)" if k != "total" and k != "trainable" else ""
print(f" {k:14s}: {v:>12,}{pct}")
print("-" * 50)
print(f"Total: {counts['total']:,} ({counts['total']/1e6:.2f}M)")
if model.rope.effective_theta != config.rope_theta:
print("-" * 50)
print(
f"NOTE: RoPE scaling ({config.rope_scaling_type} x{config.rope_scaling_factor}) "
f"is applied unconditionally from construction, not only once generation exceeds "
f"max_position_embeddings -- the theta actually running is "
f"{model.rope.effective_theta:,.0f}, not the configured {config.rope_theta:,.0f}."
)
if __name__ == "__main__":
main()
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